Stop Writing Performance-Critical Logic in Python (A Lesson From My Users)

I run a deployment platform. Recently I noticed some users' APIs were eating absurd amounts of memory and compute — response times hitting 8-10 seconds. My first thought was honestly "are these people mining crypto on my servers?" So I just... asked them.

They weren't.

What Was Actually Happening

I dug in and found the same pattern across multiple users:

They were running complex math operations inside loops — repeating the same heavy calculation on every iteration, every request, every time.

Not a logic bug. Not bad architecture. Just the wrong language for the job.

The Python Problem Nobody Talks About Enough

Python is incredible for glue code, scripting, prototyping, and ML workflows. But people forget two things:

1. The GIL (Global Interpreter Lock)
Python cannot spawn true parallel threads for CPU-bound work. If you're doing heavy computation, your threads are taking turns, not racing.

2. Garbage Collection
Python's GC is convenient but expensive. When you're allocating objects in a tight loop, the GC is working overtime and memory climbs fast.

The Fix: Compile the Heavy Parts

I told my users to pull their math-heavy and repetitive logic out of Python and write it in Rust or C++, then compile it to WebAssembly (WASM) and call it from their existing project.

The result? ~1000x performance improvement on those specific operations.

Same project. Same architecture. Just the right tool for the right job.

The Library Problem (This One Catches Everyone)

Even if you don't write low-level code yourself, your library choices are making this decision for you.

Check what your dependencies are actually written in.

Good example — cryptography (PyPI)
The performance-critical cryptographic operations are implemented in Rust. Python is just the glue. Fast, memory-efficient, production-safe.

Bad example — Python-RSA
Pure Python, top to bottom. It works. But throw it into production under real load and the GC will wreck your memory. I've seen it.

The Rule of Thumb

If a library is doing anything math-heavy, cryptographic, or repetitive under the hood — check if it's backed by C or Rust. If it's pure Python all the way down, think twice before putting it on a hot path.

This applies beyond Python too. The pattern is the same in Ruby, PHP, JavaScript — scripting languages are not built for raw computation. They're built for everything around it.

Write the glue in the language you love. Write the engine in the language that's fast.